Contextual and Sequential User Embeddings for Large-Scale Music Recommendation


Recommender systems offer great opportunity not only for users to discover new content, but also for the providers of that content to find new audience, followers, and fans. Users often come to a recommender system with certain expectations about what it will recommend to them, and a recommender system that is optimized for creating opportunities for content creators may provide recommendations that are very different from what a user is expecting. We hypothesize that some users’ expectations have a much wider range of acceptability than others, and users with more ”receptivity” to subversion of their expectations are likely to accept such divergence in the recommended content. Understanding users’ responses to such recommendations is vital to platforms that need to serve multiple stakeholders. In this work we investigate logged behavioral responses of users of an audio streaming platform to recommendations that deviate from their expectation, or “divergent” recommendations. We present three classes of listener response to divergent recommendations that can be identified in interaction logs with the aim of predicting which users can be targeted for future divergent recommendations. We derive a number of user characteristics based on user’s music consumption which we think are predictive of user’s receptivity, train models to predict receptivity of these users, and run a live A/B test to validate our approach by correlating with engagement.


December 2020 | NeuRIPS

Model Selection for Production System via Automated Online Experiments

Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas

October 2020 | CIKM

Query Understanding for Surfacing Under-served Music Content

Federico Tomasi, Rishabh Mehrotra, Aasish Pappu, Judith Bütepage, Brian Brost, Hugo Galvão, Mounia Lalmas

September 2020 | RecSys

Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions

Rishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas